Statistical Concepts, Methods and Tools

Module Identifier: MAM5120
Module Title: Statistical Concepts, Methods and Tools
Academic Year: 2026/2027
Semester: Semester 1
Co-ordinator: Dr Kim Kenobi
Reading List: View on Aspire
Other Staff: Dr Kim Kenobi; Dr Gwion Evans

Assessment

Assessment Type	Assessment details	Proportion
Semester Assessment	Report and Presentation: One assessed report of statistical analysis - 40%Presentation of results of one analysis - 10%	50%
Semester Exam	Practical Exam: 3 Hours (open book, computer-based)	50%
Supplementary Assessment	Report and Presentation: One assessed report of statistical analysis - 40%Presentation of results of one analysis - 10%	50%
Supplementary Exam	Written Exam: 2 Hours Practical examination (open book, not computer-based).	50%

Learning Outcomes

On successful completion of this module students should be able to:

Select and apply appropriate statistical methods to problems in Computational Biology.
Use a statistical package such as R to analyse Biological data.
Design experiments in a way that facilitates correct use of advanced methods for data analysis.
Interpret and report the results of analyses effectively.

Brief description

This module will provide graduates of Computer Science, Mathematics or Biological Sciences with a foundation in Statistics for Computational Biology.
Statistical concepts and ideas are presented using examples from Computational Biology. Various statistical methods are applied using the statistical package R

Content

1. Data and its presentation
2. Introduction to the statistical package R
3. Probability, conditional probability and Bayes? Theorem
4. Statistical distributions
5. Statistical models, estimation and testing.
6. t-tests and z-tests
7. Good and bad experimental design; sample size.
8. ANOVA and multiple comparisons
9. Simple, multiple and curvilinear regression
10. Chi-squared techniques and contingency tables

Module skills

Skills type	Skills details
Application of Number	Inherent in the study of statistics and statistical methods
Communication	Seminars and presentation
Improving own Learning and Performance	Self study
Information Technology	Mastery of a statistical package such as R
Problem solving	Inherent to application of statistics to problems in Computational Biology
Research skills	Experimental design. Extracting relevant information from published sources
Subject Specific Skills	Developing expertise in statistical analysis
Team work	Joint work in seminars

Notes

This module is at CQFW Level 7